Length-Aware Motion Synthesis via Latent Diffusion

Alessio Sampieri; Alessio Palma; Indro Spinelli; Fabio Galasso

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arXiv · 2407.11532

Length-Aware Motion Synthesis via Latent Diffusion

Alessio Sampieri·Alessio Palma·Indro Spinelli·Fabio Galasso

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Abstract

The target duration of a synthesized human motion is a critical attribute that requires modeling control over the motion dynamics and style. Speeding up an action performance is not merely fast-forwarding it. However, state-of-the-art techniques for human behavior synthesis have limited control over the target sequence length. We introduce the problem of generating length-aware 3D human motion sequences from textual descriptors, and we propose a novel model to synthesize motions of variable target lengths, which we dub "Length-Aware Latent Diffusion" (LADiff). LADiff consists of two new modules: 1) a length-aware variational auto-encoder to learn motion representations with length-dependent latent codes; 2) a length-conforming latent diffusion model to generate motions with a richness of details that increases with the required target sequence length. LADiff significantly improves over the state-of-the-art across most of the existing motion synthesis metrics on the two established benchmarks of HumanML3D and KIT-ML.

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Alessio Sampieri, Alessio Palma, Indro Spinelli, Fabio Galasso. 2024-07-16. Length-Aware Motion Synthesis via Latent Diffusion. https://arxiv.org/abs/2407.11532

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